An Adaptive Network Model to Simulate Consensus Formation Driven by Social Identity Recognition
Joint Authors
Zhang, Kaiqi
Lv, Zinan
Du, Hai Feng
Zou, Honghui
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-08
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Models of the consensus of the individual state in social systems have been the subject of recent research studies in the physics literature.
We investigate how network structures coevolve with the individual state under the framework of social identity theory.
Also, we propose an adaptive network model to achieve state consensus or local structural adjustment of individuals by evaluating the homogeneity among them.
Specifically, the similarity threshold significantly affects the evolution of the network with different initial conditions, and thus there emerges obvious community structure and polarization.
More importantly, there exists a critical point of phase transition, at which the network may evolve into a significant community structure and state-consistent group.
American Psychological Association (APA)
Zhang, Kaiqi& Lv, Zinan& Du, Hai Feng& Zou, Honghui. 2020. An Adaptive Network Model to Simulate Consensus Formation Driven by Social Identity Recognition. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1139952
Modern Language Association (MLA)
Zhang, Kaiqi…[et al.]. An Adaptive Network Model to Simulate Consensus Formation Driven by Social Identity Recognition. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1139952
American Medical Association (AMA)
Zhang, Kaiqi& Lv, Zinan& Du, Hai Feng& Zou, Honghui. An Adaptive Network Model to Simulate Consensus Formation Driven by Social Identity Recognition. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1139952
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139952